• Journal of Environmental Impact Assessment
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• v.22 no.6
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• pp.725-738
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• 2013

The National Ecosystem Survey in Korea provides information to policy makers for preservation of natural environment and implementation of international agreement. The 1st and 2nd National Ecosystem Survey were carried out between 1986 and 1990, and between 1997 and 2005, respectively. The 3rd National Ecosystem Survey began in 2006 and ended in 2012. In 2013 the pilot survey for the 4th National Ecosystem Survey is ongoing. The 4th National Ecosystem Survey due to the revision of the Natural Environment Conservation Act which has been done every 10 years would change into every five years. It is planned to be conducted from 2014 to 2018. The survey method of the 4th National Ecosystem Survey has been modified to obtain more accurate data for many taxa. The survey for a nocturnal animals will be introduced. In addition, monitoring by setting the grid will get quantitative data seasonally. The vegetation survey will be conducted with a mobile device contained files of aerial image maps including classified vegetation map. National Ecosystem Survey will be improved as follows. First, each survey methods suitable for the purpose should be developed. Second, monitoring methods for obtaining quantitative data should be developed. Finally, the research using the data should be developed in the field of not only ecosystem and biological diversity but also habitat assessment.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.4
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• pp.23-32
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• 2021

For agricultural water management, it is essential to establish the digital infrastructure data such as agricultural watershed, irrigated area and canal network in rural areas. Approximately 70,000 irrigation facilities in agricultural watershed, including reservoirs, pumping and draining stations, weirs, and tube wells have been installed in South Korea to enable the efficient management of agricultural water. The total length of irrigation and drainage canal network, important components of agricultural water supply, is 184,000 km. Major problem faced by irrigation facilities management is that these facilities are spread over an irrigated area at a low density and are difficult to access. In addition, the management of irrigation facilities suffers from missing or errors of spatial information and acquisition of limited range of data through direct survey. Therefore, it is necessary to establish and redefine accurate identification of irrigated areas and canal network using up-to-date high resolution images. In this study, previous existing data such as RIMS (Rural Infrastructure Management System), smart farm map, and land cover map were used to redefine irrigated area and canal network based on appropriate image data using satellite imagery, aerial imagery, and drone imagery. The results of the building the digital infrastructure in rural areas are expected to be utilized for efficient water allocation and planning, such as identifying areas of water shortage and monitoring spatiotemporal distribution of water supply by irrigated areas and irrigation canal network.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.93-101
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• 2024

Accurate field crop classification is essential for various agricultural applications, yet existing methods face challenges due to diverse crop types and complex field conditions. This study aimed to address these issues by combining support vector machine (SVM) models with multi-seasonal unmanned aerial vehicle (UAV) images, texture information extracted from Gray Level Co-occurrence Matrix (GLCM), and RGB spectral data. Twelve high-resolution UAV image captures spanned March-October 2021, while field surveys on three dates provided ground truth data. We focused on data from August (-A), September (-S), and October (-O) images and trained four support vector classifier (SVC) models (SVC-A, SVC-S, SVC-O, SVC-AS) using visual bands and eight GLCM features. Farm maps provided by the Ministry of Agriculture, Food and Rural Affairs proved efficient for open-field crop identification and served as a reference for accuracy comparison. Our analysis showcased the significant impact of hyperparameter tuning (C and gamma) on SVM model performance, requiring careful optimization for each scenario. Importantly, we identified models exhibiting distinct high-accuracy zones, with SVC-O trained on October data achieving the highest overall and individual crop classification accuracy. This success likely stems from its ability to capture distinct texture information from mature crops.Incorporating GLCM features proved highly effective for all models,significantly boosting classification accuracy.Among these features, homogeneity, entropy, and correlation consistently demonstrated the most impactful contribution. However, balancing accuracy with computational efficiency and feature selection remains crucial for practical application. Performance analysis revealed that SVC-O achieved exceptional results in overall and individual crop classification, while soybeans and rice were consistently classified well by all models. Challenges were encountered with cabbage due to its early growth stage and low field cover density. The study demonstrates the potential of utilizing farm maps and GLCM features in conjunction with SVM models for accurate field crop classification. Careful parameter tuning and model selection based on specific scenarios are key for optimizing performance in real-world applications.

• The Journal of Engineering Geology
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• v.11 no.3
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• pp.303-313
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• 2001

A cut-slope near Guam-Ri Hwado-Eup Namyangju-Si Kyunggi-Do collapsed during a heavy rainfall over 400mm at 28th of August 2000. The cut-slope collapse reportedly developed mainly by block sliding along a set of discontinuities, although slope angle of the cut-slope was 40$^{\circ}$(1:1.2) that agrees with the road construction criteria. This study aims to analyze differences and correlations among several data-collecting methods limited to discontinuity analysis related with cut-slope collapse. This study started with analysing discontinuity surface characteristics, geology of the country rock and orientations of the discontinuities directly related with the collapse. Analysis of aerial photos around the study area provided regional lineament data, and discontinuity plane description and measurements were collected from core logging and Borehole Image Processing System (BIPS). Spearmans correlation ranking coefficient method was used to get correlation of discontinuity planes according to analysis methods. The result suggests that the correlation coefficient is ${\gamma}_s$ = 0.91 Plus, stability analysis of discontinuity plane orientation data using equal-area stereonet revealed that the study area is unstable to planar failure. This study suggests that the cut-slope angles currently applied should be shallower and that significant attention is required to orientation distribution of discontinuities existed in cut-slopes studies.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.17 no.1
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• pp.11-19
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• 1999

Lately, with the development of the fields of computer and photogrammetry, Digital Photogrammetric Systems are widely used for the generation of GIS basemap, the acquisition of topographic information and DEM, the formation of digital orthophoto, three-dimensional viewing and so on. According as the demand for the systems is rapidly increasing, we suggest keenly the necessity of domestic technical development, because all of these systems depend on foreign technology until now. In this study, by using digital photogrammetry method, with Visual C++ language, we have developed Digital Photogrammetric Systems for Windows which is able to get three-dimensional coordinates through interior orientation, exterior orientation, epipolar line, image matching from a pair of aerial photos taken with metric camera. This system consists of not only a module which can revise digital map that is being made at National Geographic Institute as a part of data construction project of National Geographic Information System, but also a module which can view three-dimensional image on the screen monitor by using anaglyph for three-dimensional analysis. The digital photogrammetry modules developed in this study are expected to be used as primary modules for the effective management of the urban as well as main modules in developing professional digital photogrammetric systems.

• Korean Journal of Remote Sensing
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• v.30 no.2
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• pp.207-218
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• 2014

The information on the land cover along stream corridor is important for stream restoration and maintenance activities. This study aims to review the different classification methods for mapping the status of stream corridors in Seom River using airborne RGB and CIR digital ortho imagery with a ground pixel resolution of 0.2m. The maximum likelihood classification, minimum distance classification, parallelepiped classification, mahalanobis distance classification algorithms were performed with regard to the improvement methods, the skewed data for training classifiers and filtering technique. From these results follows that, in aerial image classification, Maximum likelihood classification gave results the highest classification accuracy and the CIR image showed comparatively high precision.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.6
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• pp.155-163
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• 2019

This paper proposes a new methodology to recognize cracks on asphalt road surfaces using the image data obtained with drones. The target section was Yuseong-daero, the main highway of Daejeon. Furthermore, two object detection algorithms, such as Tiny-YOLO-V2 and Faster-RCNN, were used to recognize cracks on road surfaces, classify the crack types, and compare the experimental results. As a result, mean average precision of Faster-RCNN and Tiny-YOLO-V2 was 71% and 33%, respectively. The Faster-RCNN algorithm, 2Stage Detection, showed better performance in identifying and separating road surface cracks than the Yolo algorithm, 1Stage Detection. In the future, it will be possible to prepare a plan for building an infrastructure asset-management system using drones and AI crack detection systems. An efficient and economical road-maintenance decision-support system will be established and an operating environment will be produced.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.2
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• pp.261-271
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• 2009

Due to the recent rise of a need for 3 dimensional geospatial information on urban areas, general interest in aerial multi-view cameras has been on an increase. The conventional geospatial information system depends solely upon vertical images, while the multi-view camera is capable of taking both vertical and oblique images taken from multiple directions, thus making it easier for the user to interpret the object. Through our research we developed a prototype of a multi-view camera system that includes a camera system, GPS/INS, a flight management system, and a control system. We also studied and experimented with the camera viewing angles, the synchronization of image capture, the exposure delay, the data storage that must be considered for the development of the multi-view camera system.

• Korean Journal of Remote Sensing
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• v.22 no.3
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• pp.183-197
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• 2006

Combinations of visible and near-infrared (NIR) bands in an image are widely used for estimating vegetation vigor and productivity. Using this approach to understand within-field grain crop variability could allow pre-harvest estimates of yield, and might enable mapping of yield variations without use of a combine yield monitor. The objective of this study was to estimate within-field variations in crop yield using vegetation indices derived from hyperspectral images. Hyperspectral images were acquired using an aerial sensor on multiple dates during the 2003 and 2004 cropping seasons for corn and soybean fields in central Missouri. Vegetation indices, including intensity normalized red (NR), intensity normalized green (NG), normalized difference vegetation index (NDVI), green NDVI (gNDVI), and soil-adjusted vegetation index (SAVI), were derived from the images using wavelengths from 440 nm to 850 nm, with bands selected using an iterative procedure. Accuracy of yield estimation models based on these vegetation indices was assessed by comparison with combine yield monitor data. In 2003, late-season NG provided the best estimation of both corn $(r^2\;=\;0.632)$ and soybean $(r^2\;=\;0.467)$ yields. Stepwise multiple linear regression using multiple hyperspectral bands was also used to estimate yield, and explained similar amounts of yield variation. Corn yield variability was better modeled than was soybean yield variability. Remote sensing was better able to estimate yields in the 2003 season when crop growth was limited by water availability, especially on drought-prone portions of the fields. In 2004, when timely rains during the growing season provided adequate moisture across entire fields and yield variability was less, remote sensing estimates of yield were much poorer $(r^2<0.3)$.

• Ecology and Resilient Infrastructure
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• v.8 no.4
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• pp.266-272
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• 2021

Water friendly space refers to a place designated to plan and manage spaces for residents Water friendly activities. Efficient management of river Water friendly parks requires automated GIS data and DB construction of the water friendly facilities. Object-based classification using drone images or aerial images is attracting attention as an efficient means to acquire 3D spatial information in the country. To remove the miscellaneous image included in the extracted outline, a linear simplification of the outline is required, and it is difficult to apply manually, so various automation methods have been developed to overcome this, and among them, the most widely studied and utilized is the linear simplification method. In this study, the suitability of linear simplification algorithms such as Douglas-Peucker, Visvalingam-Whyatt, and Bend-simplify algorithms for the geometric shape of hydrophilic facilities was determined.